A review on the recent applications of deep learning in predictive drug toxicological studies
Abstract
Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.
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Researchers have developed predictive models that estimate the safety of chemicals and their potential effects on human health.
“Researchers have developed predictive models that estimate the safety of chemicals and their potential effects on human health (139).”
From The dark side of beauty: an in-depth analysis of the health hazards and toxicological impact of synthetic cosmetics and personal care products (2024) · cites this paper as [139]
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- Authors
- Sinha, K., Ghosh, N., & Sil, P. C.
- Affiliations
- Jhargram Raj College (IN)
- Maulana Azad College (IN)
- Bose Institute (IN)
- Corresponding author
- Sinha, Krishnendu
- Sil, Parames C.
- Venue
- Chemical Research in Toxicology
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-08-10
- Volume
- 36
- Issue
- 8
- Pages
- 1174-1205
- ISSN
- 0893-228X (ISSN-L)
- 1520-5010
- OpenAlex
- W4385741356
- Cited by
- 64
- References
- 251
- Citations by year
- 2026: 20 · 2025: 25 · 2024: 15 · 2023: 4
- FWCI
- 1.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Chemical Research in Toxicology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- drug toxicity prediction, predictive toxicology, deep learning, animal use reduction, preclinical toxicity testing, automated feature engineering, drug-drug interactions, regulatory acceptance, model interpretability, high-throughput screening data, genomic data, drug safety assessment
- MeSH terms
- Deep Learning, Animals, Drug Interactions, Genomics, Drug Discovery, Drug-Related Side Effects and Adverse Reactions
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